A universal and cost-effective method for the mitigation of interferences in inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
A universal and cost-effective method utilizing low sample uptake rate (50 μL min −1 ) combined with a mixed-gas plasma containing 1.1 % nitrogen is demonstrated to reduce oxide, carbon, and argide interferences while mitigating signal suppression from complex matrices. Low sample uptake rate also reduced nitrogen-based interferences in an argon plasma. On average, oxide interferences (ScO/Sc, YO/Y, ZrO/Zr, BaO/Ba) decreased by 94.0 % when comparing an argon plasma with 1 mL min −1 sample uptake rate to 50 μL min −1 in a mixed-gas plasma. Carbon-based, nitrogen-based and argide interferences decreased by 50.6 % to 95.5 % depending on plasma condition, sample uptake rate, and interference type. Furthermore, matrix-based signal suppression arising from 100 mg L −1 Na, Rb or Cs on 50 μg L −1 Li, Mg, Cr, Mn, Co, Sr, Y, and Pb were almost fully mitigated under both plasma conditions at low sample uptake rate, with an increase in matrix effect mitigation observed in the mixed-gas plasma. By minimizing sample waste and wear on costly instrument components, the enclosed method offers a greener solution for analytical laboratories, decreasing operational costs and increasing sample throughput without introducing significant error or uncertainty. Ultimately, this work can be universally implemented, without the need for costly instrument modifications or consideration for instrument make or model. • A cost effective and universal method for mitigation of interferences is provided. • Enclosed method reduces Sc, Y, Zr, and Ba oxide interferences by 94.0 %. • Carbon-based, nitrogen-based and argide interferences decreased by 50.6 % to 95.5 %. • Almost full mitigation of matrix-based signal suppression arising from Na, Rb or Cs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".